Menu Mandates and Obesity: A Futile Effort

One provision of the Patient Protection and Affordable Care Act
(ACA) that has been delayed until 2017 is a federal mandate for
standard menu items in restaurants and some other venues to contain
nutrition labeling. The motivation for so-called “menu
mandates” is a concern about rising obesity levels driven
largely by Americans’ eating habits. Menu mandates have been
implemented at the state and local level within the past decade,
allowing for a direct examination of the short-run and long-run
effects on outcomes such as body mass index (BMI) and obesity.
Drawing on nearly 300,000 respondents from the Behavioral Risk
Factor Surveillance System (BRFSS) from 30 large cities between
2003 and 2012, we explore the effects of menu mandates. We find
that the impact of such labeling requirements on BMI, obesity, and
other health-related outcomes is trivial, and, to the extent it
exists, it fades out rapidly. For example, menu mandates would
reduce the weight of a 5’10” male adult from 190 pounds to
189.5 pounds. For virtually all groups explored, the long-run
impact on body weight is essentially zero. Analysis of subgroups
suggests that to the extent that menu mandates affect short-run
outcomes, they do so through a “novelty effect” that
wears off quickly. Subgroups that were thought likely to experience
the largest gains in knowledge from such mandates exhibit no
short-run or long-run changes in weight.

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Menu Mandates and Obesity

Introduction

The prevalence of obesity has increased markedly in the United
States over time and has affected all socioeconomic
groups.1, 2, 3 Although
the estimated cost of obesity—in terms of disease, medical
visits, lost work days, and other outcomes—varies widely,
some have argued that these costs represent a rationale for
government intervention to reduce obesity-related
externalities.4, 5

The Patient Protection and Affordable Care Act is the most
significant government overhaul of the U.S. healthcare system since
the passage of Medicare and Medicaid in the 1960s. One often
overlooked provision, Section 4205, mandates that calorie
information be provided on menus of restaurants and numerous other
venues.6 When fully implemented,
this “menu mandate” will affect 300,000 establishments,
and the breadth of the Food and Drug Administration’s (FDA)
final rule surprised even health advocates.7, 8 Chain
restaurants, movie theaters, grocery stores (for their salad or hot
bars), and vending machines will be forced to provide calorie
counts. Although this FDA regulation was supposed to be effective
in December 2015, it was pushed back and will now be implemented in
2017.9

The stated motivation for such menu mandates is to reduce the
number of overweight and obese Americans by reducing their
consumption of calories. A significant portion of food expense and
calories comes from foods prepared outside the home, and government
officials believe that many people do not know (and may
underestimate) the caloric content of such food.10 The federal mandate was preceded by similar
efforts at the state and local levels within the past decade,
perhaps the best known of which was New York City’s menu
mandate in 2008. At the time, some argued that menu mandates could
lead to substantial reductions in weight—roughly 7.5 pounds
per year, or 106 calories per fast-food transaction.11

To date, the most convincing evidence concerning the effects of
menu mandates—both in New York City and elsewhere—has
been on calorie consumption associated with individual
transactions. The evidence on the broader effectiveness of such
mandates is mixed; we discuss it later. However, more important
than any one transaction is whether menu mandates have any
long-lasting impact on body weight or obesity. The principal
contribution of this analysis is to explore this issue by using
publicly available data on nearly 300,000 respondents from the
Behavioral Risk Factor Surveillance System for 30 large cities
between 2003 and 2012. On a staggered basis, some of these cities
implemented menu mandates, while others did not. This paper finds
that the impact of such mandates on body weight is trivial, and to
the extent an impact exists, it fades out rapidly. For example,
menu mandates would reduce the weight of a 5’10” male adult from
190 pounds to 189.5 pounds. For virtually all groups explored, the
long-run impact on body weight of menu mandates is essentially
zero. This evidence demonstrates the futility of government efforts
at altering individuals’ preferences regarding the food they
eat; the lack of benefit, in conjunction with costs both to
consumers and businesses, shows that government-imposed menu
mandates are ill-advised.

The Benefits of Menu Mandates

Bollinger et al. discuss the potential impact of menu
mandates.12 Learning information
about calories contained in food and beverages may lead to
healthier purchases by consumers at chain restaurants. However,
customers may care mostly about convenience, price, and taste, with
calories being relatively unimportant. It may also be the case that
those who do care about calories are already well-informed; such
nutrition information is available for the motivated customer.

How menu mandates affect behavior in the long run, or outside of
the restaurant setting, is less clear. With respect to long-run
behavior, such mandates may improve a customer’s knowledge of
calories (a “learning effect”) or sensitivity to
calories (a “salience effect”).13 To the extent that menu mandates improve
learning and correct misperceptions about food calories, the
effects of menu mandates are more likely to be permanent. To the
extent they simply make calories more salient, the effects are more
likely to be short-lived. Efforts to make unwanted information more
salient—from web banners to graphic tobacco warning
labels—tend to be ineffective, especially after the initial
novelty wears off.14 Cantor et
al. surveyed consumers in New York City immediately after the menu
mandate took effect in 2008, and at three points during
2013-2014.15 They found that the
percentage of respondents noticing and using the information
declined in each successive period, and that there were no
statistically significant changes in calorie levels or visits to
fast-food restaurants.

In addition to these responses, it is also possible that
restaurants innovate by offering more low-calorie items in the long
run, making the mandate more impactful. Outside the restaurant
setting, consumers’ exposure to calorie information may make
them generally more aware and attentive to the nutritional value of
the foods they eat.16 On the
other hand, people may offset changes in their calorie consumption
at restaurants by changing what they eat at home.

Ultimately, the evidence on the effects of menu mandates on
caloric intake at restaurants is mixed. Studying the implementation
of the menu mandate in New York City, Bollinger et al. find that
average calories consumed per transaction at Starbucks fell by 6
percent, but that this change disproportionately affected consumers
who made high-calorie purchases (thereby potentially having a
larger impact on obesity rates).17 Yet a meta-analysis by Long et al. found
that “current evidence does not support a significant impact
on calories ordered.”18 And
the findings of Cantor et al. suggest any effects may be
short-lived. While reduced caloric intake at point-of-purchase is
certainly a necessary condition for reductions in body weight, it
is not sufficient. As mentioned previously, the stated goal is to
reduce the prevalence of being obese and overweight, especially in
the long run. Thus the focus of this study is much more accurately
aligned with the explicit public health policy goal of such menu
mandates. A recent paper by Deb and Vargas explores many of the
same issues as this paper; the authors use the BRFSS and the
staggered implementation of menu mandates to examine effects on
BMI, although the geographic coverage and econometric methods
differ.19 In many respects, the
principal findings of the two studies are quite similar: for the
population as a whole, the effects of menu mandates on BMI are very
small. Deb and Vargas find significant effects for some subgroups,
as does this study. And although not the key focus of their study,
their entropy-balanced, weighted trends for men (where they do find
significant effects) show convergence by 2012, consistent with a
fade-out effect of menu mandates found in this study.

The Costs of Menu Mandates

Some of the same studies that find reductions in calories
consumed (arguably a benefit) also assert that the costs of menu
mandates are trivial or nonexistent. For example, Bollinger et al.
argue that “as far as regulatory policies go, the costs of
calorie posting are very low—so even these small benefits
could outweigh the costs.”20 This section reviews the costs of menu
mandates.

Some of the financial costs are outlined in Bollinger et al. One
cost of menu mandates is updating display menus, which is modestly
expensive. This potentially is a one-time fixed cost, and perhaps a
primary reason many chains are switching to digital menu
boards.21 Another cost is
determining the caloric content of each menu item. This is likely a
more important issue for the other types of venues covered by the
menu mandate, as most chains know well the caloric content of each
regular menu item.22 Related to
this, there may be increased legal costs from being exposed to
potential litigation if the posted calories are incorrect. Menu
mandates may also affect operating profits by decreasing demand or
frequency of visits, but this was not the case for
Starbucks.23

There are also more subtle costs. Adding calorie content can
slow down the ordering process, which reduces the overall
convenience of consuming fast food. Some menu labeling laws
distinguish between—and have different requirements
for—menu boards inside a restaurant and drive-through menus
outside a restaurant. For example, California’s statewide
menu mandate (effective January 2011) required menu boards to
display calories next to the item, but allowed to drive-throughs to
offer a brochure that is available on request.24 This law implicitly recognizes the
potential bottleneck that arises with one line in a drive-through
setting, but the same critique about reduced convenience applies
inside the store as well.

Arguably a more important, but harder to measure, cost is the
reduced utility from consuming a meal. Although Cantor et al. find
increases of up to 37 percentage points in those who saw calorie
labels in New York City after the menu mandate (from 14 percent to
51 percent), those who used labels to order fewer calories
increased by just 7 to 10 percentage points. Among those who see
such information but do not use it in altering their purchasing
choices, such “education” presumably lowers utility for
those who still consume high-calorie meals anyway. Glaeser calls
this an “emotional tax” on behavior that yields no
government revenue, just pure utility losses.25

Empirical Approach and Findings

Although one cannot yet measure the impact of the menu mandate
provision in the ACA, a number of localities and states have
regulated menu information at chain restaurants since 2008. Most
prominently, in New York City under Mayor Michael Bloomberg,
efforts were made to regulate soda sizes, limit trans fats, and
mandate calorie disclosure on menus, leading to calls of New York
becoming a “nanny state.”26 Although receiving far less attention, some
of these same measures—especially regarding mandated calorie
disclosure—were implemented in a number of other large urban
areas, including Philadelphia, Portland, Seattle, as well as
statewide in Massachusetts and California. The empirical approach,
discussed in the appendix, is to compare individuals in these
locations both before and after menu mandates were enforced. To
address concerns that other factors besides menu mandates may also
affect body weight and were changing over time, other large cities
(Charlotte, Chicago, Columbus, Dallas, Denver, Detroit, El Paso,
Fort Worth, Houston, Indianapolis, Jacksonville, Louisville,
Memphis, Milwaukee, Nashville, Oklahoma City, Phoenix, and San
Antonio) serve as a control group.

The analysis relies on transparent, publicly available data from
the Behavioral Risk Factor Surveillance System. The BRFSS completes
more than 400,000 adult interviews each year, making it the largest
continuously conducted health survey system in the
world.27 Between 2003 and 2012,
the publicly available data both identify an individual’s
locality and ask about body weight.28 In virtually all studies of adults, the
critical outcome of interest is the body mass index, which is a
measure of body fat based on height and weight: BMI is a
person’s weight in kilograms divided by the square of their
height in meters. From there, various thresholds of BMI are used to
classify individuals as obese (BMI >= 30.0), overweight (BMI
>= 25.0), underweight (BMI < 18.5), or normal weight (18.5
<= BMI < 25.0).29

Without a doubt, the largest share of attention has been focused
on obesity. More than one-third of adults in the United States are
obese.30 The empirical analysis
in this paper examines the impact of menu mandates on obesity,
along with the other body weight outcomes (BMI levels, overweight
or more, underweight). Because of the motivations discussed earlier
about learning and salience (i.e., the “novelty” of
calorie disclosure), this study estimates both the immediate impact
and the longer-run impact of menu mandates. Figure 1 Figure 1, “The Effect of Menu Mandates on Obesity
Levels is Short-Lived” illustrates how menu mandates
affect obesity rates for adults in the years after enactment, using
coefficient estimates from the regression model based on all 30
cities in Table A.2. Table A.2, “Full
Sample from the Behavioral Risk Factor Surveillance
System”

Figure 1. The Effect of Menu Mandates on Obesity Levels
is Short-Lived

Source: Effects from regression model were estimated by the
author from the Behavioral Risk Factor Surveillance System data in
Table 2, Specification 2.

As can be seen, prior to enactment of menu mandates (period -1),
approximately 25.7 percent of adults in these 30 major cities were
obese. There is a statistically significant reduction in obesity at
time of implementation—roughly 1.25 percentage
points—which would bring down the obesity rate to 24.5
percent. However, the effects are short-lived. In years after
enactment, the novelty of menu mandates appears to wear off, and
obesity rates again rise, such that the entire impact on obesity
disappears within four years. Thus, menu mandates appear to have a
small but temporary impact on obesity.

In addition to obesity, where the effects fade over time, the
study considered BMI, for which there appear to be more permanent
effects, although these effects are not concentrated amongst the
heaviest individuals. The same empirical models show that menu
mandates lead to a one-time reduction in BMI of 0.15 BMI points,
and that this weight reduction is sustained over time. Figure 2
Figure 2, “The Impact of Menu Mandates on
Body Weight” illustrates the practical importance of this
reduction.

Figure 2. The Impact of Menu Mandates on Body
Weight

Source: Author’s calculations from model estimated from
the Behavioral Risk Factor Surveillance System data in Table 2,
Specification 2. Results were calculated for the average BMI in the
sample of 27.3.

Although the weight loss is statistically significant, an effect
of 0.15 BMI points translates into a barely noticeable difference
in weight. For example, for an individual who is 5’10” and is
initially average (BMI = 27.3), the reduction in body weight is
roughly one pound. As can be seen for heights that vary from 5’0”
to 6’0”, the impact of menu mandates for the typical individual is
hardly visible.

The technical analysis in the appendix further examines the
effects of menu mandates among various socioeconomic groups. It
finds that the impacts are nonexistent for young adults and the
less educated—both groups where, it could be argued, that
such mandates convey new and meaningful information about caloric
content. In contrast, the effects (and fade-out) are larger for
older adults and those with more education—both groups that
likely have greater knowledge of caloric content, and where such
mandates provide salience, novelty, or guilt when initially
implemented. For them, there are larger initial reductions in BMI
and obesity, but the initial effects fade out quickly. The
conclusion that emerges is that menu mandates serve as an
ineffective “emotional tax.”

Conclusion

The analysis in this study has found that menu mandates are a
futile effort to reduce body weight, with trivial or short-lived
effects on BMI and obesity. What public efforts should be
undertaken to reduce obesity? The intuitive answer is
“nothing at all.” People make choices about all aspects
of their lives. Whether it is to eat unhealthily, smoke cigarettes,
use drugs, consume alcohol, drop out of school, watch too much
television, not exercise, or not save for retirement, all of these
decisions should ultimately fall onto the individual, who has to
live with the consequences of his or her actions. In virtually all
of these cases, as illustrated with BMI and obesity in this study,
the argument that individuals are ill-informed about the
consequences of their actions is implausible.

Proponents of government intervention would argue that there are
negative externalities—costs of obesity that are not borne by
the individual, but by society as a whole. The primary consequences
of obesity are costs related to disease, medical visits, and lost
work days. In principle, each of these would be internalized by the
individual through well-functioning insurance and labor markets.
That is, the fact that Medicare or Medicaid costs increase due to
obesity is not a problem about obesity, but about public health
insurance not accurately pricing premiums to reflect an
individual’s choices. Private, unregulated insurance markets
would price their products based on such risk characteristics, in
which case such externalities are internalized.

Finally, some prominent behavioral economists look at the
evidence on ineffectiveness of calorie labeling and suggest
doubling down. Cass Sunstein has recently argued that menu mandates
are too complicated and argues for “simple and
meaningful” disclosures to consumers, such as putting a
“red light” on highly caloric foods and a “green
light” on the healthier ones.31 The current analysis shows that the problem
is not lack of knowledge or conveying information—on the
contrary, the consumers who responded to the menu mandates were
among the most knowledgeable. Rather, people have preferences that
are more or less fixed, and for the most part, people enjoy
cheeseburgers more than broccoli. The private market provides ample
nutrition advice at extremely low cost, from cell-phone apps that
give calorie and other nutrition information to easy-to-understand,
simple substitutions in books such as Eat This, Not That. There is
no need for government-mandated disclosures that impose an
emotional tax on each transaction when individuals can easily and
voluntarily seek out such information on nutrition.

A. Appendix

Data

The analysis uses data from the Behavioral Risk Factor
Surveillance System.32 The BRFSS
is a collaborative project of the Centers for Disease Control and
Prevention (CDC) and U.S. states and territories.33 The BRFSS, administered and supported by
CDC’s Behavioral Risk Factor Surveillance Branch, is an ongoing
data-collection program designed to measure behavioral risk factors
for the noninstitutionalized adult population (18 years of age and
older). The BRFSS was initiated in 1984, with 15 states collecting
surveillance data on risk behaviors through monthly telephone
interviews. Over time, the number of states participating in the
survey increased: by 2001, all 50 states, the District of Columbia,
Puerto Rico, Guam, and the U.S. Virgin Islands were participating
in the BRFSS.

Of critical importance, the BRFSS calculates the body mass index
from the respondent’s reported height and weight. The BMI is a
measure of body fat based on height and weight that applies to
adult men and women, where a BMI of 30.0 or greater is classified
as obese, a BMI between 25.0 and 29.9 is classified as overweight,
a BMI between 18.5 and 24.9 is classified as normal weight, and a
BMI less than 18.5 is classified as underweight.34

The BRFSS consists of repeated annual cross sections of randomly
sampled adults. The survey boasts a large number of respondents,
which is critical to obtaining meaningful precision when examining
the impact of a local program where effects might be concentrated
amongst only a fraction of the population.35 Given the focus on local regulations
regarding caloric content, the analysis uses BRFSS data from 2003
to 2012, where county identifiers are included.36 Adults in the 30 largest cities in the
United States are included, reducing the initial BRFSS sample from
3,991,585 observations to 362,361 observations.37 The total population in these cities,
approximately 38.97 million in July 2012, is 12.4 percent of the
total U.S. population.38 These 30
cities include New York, Philadelphia, Seattle, and Portland, all
of which mandated calorie disclosure on menus starting in 2008 or
later. The cities also include Los Angeles, San Francisco, San
Jose, San Diego, and Boston, where state legislation mandated
disclosure. By 2012, nearly half the residents of these 30 large
cities were covered by such mandates. The final sample consists of
adults aged 18 and over who provided sufficient information to
compute BMI; demographics (race, ethnicity, age, gender, number of
children, and marital status); socioeconomic status (education,
employment, and income); and health status (self-reported health
and exercise). These restrictions reduce the sample to 288,392
individual-level observations that are used in the empirical
analysis.

Summary Statistics

The vast majority of menu mandates were implemented at the local
level by very large cities. A natural concern, one that is
accounted for in the regression framework with city-fixed effects,
is that large cities differ from smaller cities or rural areas, and
also that large cities with calorie disclosure requirements differ
from other ones that did not have such mandates. Table A.1 Table A.1, “Comparisons across 30 cities over
time in Body Mass Index (BMI), Obesity, and Health
Habits” provides summary statistics in 2003 and 2012
across the 30 cities for several key health variables.39 The BMI and obesity (BMI >= 30.0)
increased in almost every city over this period. There is
significant cross-sectional variation in residents’ weight
prior to any menu mandates: Detroit, Louisville, and San Antonio
had obesity rates exceeding 30 percent in 2003, while many of the
cities that subsequently mandated calorie disclosure had obesity
rates below 20 percent.

Such differences in BMI or obesity could reflect fixed
characteristics at the local level, such as the weather (and ease
of exercising outdoors) or mode of transport to work, and are
controlled for in the empirical work with city-fixed effects. Put
differently, it is likely that the localities that forced calorie
disclosure are different in other ways. This is illustrated in
Table A.1 by looking at self-reported health and exercise habits,
both of which exhibit significant cross-sectional variation: in
2003, there is approximately a 20 percentage point difference in
reporting any exercise in the past 30 days between the least active
and most active cities.

Empirical Specification

The staggered implementation of menu mandates in some
localities, but not others, creates a straightforward
“difference-in-difference” framework that has been
effectively used to estimate the causal effect of
policy.40 The regression
specification is set up as follows:

where WEIGHTijt represents BMI, Obesity, Overweight,
or Underweight for person i in city j (for the 30 cities listed in
Table A.1 Table A.1, “Comparisons across
30 cities over time in Body Mass Index (BMI), Obesity, and Health
Habits”) in time period t (2003-2012), and is a
continuous measure for BMI, or a dummy variable equal to 1 if the
individual was Obese (BMI >= 30.0), Overweight (BMI >= 25.0)
or Underweight (BMI < 18.5). Also included are individual
controls in Xi related to the respondent’s age, education,
race/ethnicity, gender, marital status, health status, exercise
frequency, and number of children.

The variable MENU_MANDATEjt is a policy indicator
that varies by city and time period, and is equal to 1 if the
locality mandated calorie disclosure in year t. Additionally, the
variable YEARS_AFTERjt measures the number of years since the
mandate was implemented. At the local level, New York,
Philadelphia, Seattle (King County), and Portland (Multnomah
County) passed menu mandates.41
At the state level, California, Maine, Massachusetts, and Oregon
passed such mandates.42 For
example, New York City implemented a mandate in 2008; thus both
variables equal 0 for these residents in the years 2003-2007, while
MENU_MANDATEjt equals 1 for the years 2008-2012, and YEARS_AFTERjt
increases from 1 in 2008 to 5 by 2012. The mandate variables are
constructed at the group level, while the BRFSS data itself is at
the individual level. Following the recommendation of Cameron,
Gelbach, and Miller, the standard errors are corrected for
non-nested two-way clustering, where the clustering is based on
locality and year.43

Note: Summary statistics are unweighted and include adults aged
18 and over. Cities with an asterisk had implemented a mandate for
chain restaurants to disclose calories by 2012. The BMI is average
Body Mass Index and Obese is the fraction with BMI >= 30.0. Good
health is the fraction of sample that self-reports health status as
excellent, very good, or good. Any exercise is the fraction that
reports any exercise (outside of work) in the past 30 days.
Individuals with invalid data on any question excluded from
table.

Main Results and Subgroup
Analysis

Table A.2 presents results for four outcomes: BMI, Obese,
Overweight, and Underweight. The first set of results includes an
indicator for a menu mandate, but not additional years since
passage. Although not shown, all specifications include dummy
variables for year and city, interview month, health status,
gender, marital status, race/ethnicity, any exercise, education,
and age and number of children. In no case are the results
statistically significant. In addition, if one were to interpret
the point estimate on BMI, it would indicate that the effect of
menu labeling reduces BMI by 0.11 points. The average respondent in
the sample has a BMI of 27.3, suggesting such labeling would reduce
BMI to approximately 27.2. To put this in perspective, for a 5’10”
male adult, this translates into a reduction in weight from 190
pounds to 189.5 pounds, roughly a 0.5 pound reduction.47 None of the threshold measures—Obese,
Overweight, or Underweight—are significant.

Table A.2. Full Sample from the Behavioral Risk Factor
Surveillance System

Notes: Standard errors in parentheses and are corrected for
non-nested two-way clustering, using the methods of A. Colin
Cameron, Jonah B. Gelbach, and Douglas L. Miller, “Robust
Inference with Multiway Clustering,” Journal of Business
& Economic Statistics 29, no. 2 (2011): 238-49, where
clustering is grouped on city and year. All specifications includes
fixed effects for city (30 overall), year (2003-2012), and
interview month. Individual covariates include self-reported health
(excellent/very good/good)(omitted is fair/poor); male; married;
race/ethnicity (Hispanic, white, African-American)(omitted is other
group); any exercise in past 30 days; number of children; education
(high school or less, some college)(omitted is college graduate);
and age.

The second set of findings examines the full sample, and
includes both an indicator for the menu mandate as well as years
since passage. For BMI, the initial implementation significantly
reduces BMI (p-value of 0.037). However, the interpretation is much
the same as before, as the effect of menu labeling reduces BMI by
0.15 points. The effect appears to be long-lived for the full
sample, as the effect of years-since-passage is insignificant.

Perhaps the most noteworthy result relates to obesity (BMI >=
30.0). In the full sample, nearly 26 percent of respondents are
obese. The immediate impact of menu labeling is to significantly
reduce obesity by nearly 1.3 percentage points (p-value of 0.016).
Bollinger et al. argue that if the policy goal is to address
obesity, it is important to know whether calorie posting
disproportionately affects consumers who make high-calorie
purchases.48 They find that
calorie posting has a large influence on Starbucks loyalty
cardholders who tended to make high-calorie purchases. For
consumers who averaged more than 250 calories per Starbucks
transaction, calories per transaction fell by 26 percent, versus 6
percent for the full sample. The short-run effect estimated from
the BRFSS analysis appears consistent with Bollinger et
al.49 However, the effect on
obesity is short-lived, as the coefficient on years-since-passage
is positive. Each additional year since passage increases obesity
by nearly 0.4 percentage points, meaning that the short-run
reduction in obesity disappears within four years. Menu labeling
mandates have no long-run impact on obesity. Furthermore, menu
mandates have no impact on Overweight (BMI >= 25.0, comprising
nearly 61 percent of the full sample) or Underweight (BMI <
18.5, comprising 1.7 percent of the full sample).

Table A.3 Table A.3, “Learning versus
Salience” breaks out the full sample into various
subgroups that may be of interest in their own right. Even though
the effects of menu mandates are ineffective for the full sample,
they may be more significant for various groups. As Bollinger et
al. (2011) explain, two of the principal methods through which menu
mandates may reduce weight are learning effects and salience
effects.50 It is reasonable to
believe that the learning effect would be more important for those
with less experience with nutrition, where two proxies for such
inexperience are low levels of education and young age. Conversely,
the learning effect should be less important for those with more
experience with nutrition, which is proxied by higher levels of
education and older ages. If learning is unimportant for these
groups, then any effects on weight are likely due to the salience
of caloric information on menus.

Notes: Standard errors in parentheses and are corrected for
non-nested two-way clustering, using the methods of A. Colin
Cameron, Jonah B. Gelbach, and Douglas L. Miller, “Robust
Inference With with Multiway Clustering,” Journal of Business
& Economic Statistics29, no. 2 (2011): 238-49, where clustering
is grouped on city and year. All specifications includes fixed
effects for city (30 overall), year (2003-2012), and interview
month. Individual covariates are identical to that in Table 2,
except when stratifying on covariate under consideration.

The first panel of this table examines weight outcomes for those
with a high school diploma or less, and the second examines
outcomes for young adults aged 18 to 29. For both groups, menu
mandates are more likely to convey new information. For less
educated individuals, there is no evidence that mandates influence
weight, suggesting that the information effect plays a relatively
minor role. For young adults, it does appear that mandates reduce
obesity, and that such an impact grows over time. When the two
groups are combined—less educated young adults—there
appears to be some sustained effect on Overweight but no effect on
Obesity or Underweight.

The second panel examines weight outcomes for respondents with
at least some college education, as well as older adults. In these
groups, one may speculate that salience of caloric content plays a
more important role. The results for more-educated respondents are
striking. The immediate effect of menu mandates is a BMI reduction
of nearly 0.28 BMI points (p-value of 0.004), but the effect
disappears within approximately four years (with BMI increasing by
nearly 0.06 BMI points per year, p-value of 0.053). Menu mandates
have long-lasting effects on Overweight, but short-lasting effects
on Obesity. For Overweight, the immediate effect is a reduction of
1.4 percentage points (p-value of 0.056), and the effect does not
diminish over time. For Obesity, there is a large immediate
reduction of 1.7 percentage points (p-value of 0.044), but this
effect is eliminated within approximately three years (with Obesity
rising by 0.5 percentage points per year, p-value of 0.042). There
is no effect for Underweight. The findings are similar for
individuals aged 30 and over. The immediate and long-lasting effect
on BMI is to reduce it by 0.18 BMI points (p-value of 0.028). As
with more educated individuals, the immediate impact on Obesity is
significant but short-lived. The immediate reduction is 1.4
percentage points (p-value of 0.017), but the effect also
disappears within three years (with Obesity rising by 0.5
percentage points per year, p-value of 0.001). As before, effects
on Overweight appear to be longer lasting, and there is no effect
on Underweight.

By combining the two groups—college-educated individuals
aged 30 and over—the fade-out effects become extremely
apparent. The immediate effect of menu mandates reduces BMI by 0.3
BMI points (p-value of 0.002) but BMI subsequently increases by
0.07 points per year (p-value of 0.018). There again appear to be
sustained effects on Overweight, but effects on Obesity fade out
quickly.

Table A.4 Table A.4, “Intensive Users
of Fast Food” breaks outs the sample into those who are
likely more intensive users of chain restaurants. Driskell et al.
show that a significantly higher percentage of male college
students report eating fast foods at least once a week relative to
female college students.51 Other
work shows that unmarried men spend a significantly greater
proportion of their food budget on commercially prepared food than
their married male peers. Households headed by single men spent
more per capita on such food than those headed by single
women.52

Given these findings, it is expected that single men would
likely be more intensive users of fast food. However, the impact of
menu mandates is less clear. It is surely the case that menu
mandates should not matter for those who tend to cook at home. Yet
among regular users of fast food, it is possible that much of the
learning about caloric intake has already been done, or that food
choices are relatively ingrained. The findings in the first panel
of Table A.4 Table A.4,
“Intensive Users of Fast Food” show no impact of
menu mandates on unmarried men across BMI and each weight
category.

Notes: Standard errors in parentheses and are corrected for
non-nested two-way clustering, using the methods of A. Colin
Cameron, Jonah B. Gelbach, and Douglas L. Miller, “Robust
Inference With with Multiway Clustering,” Journal of Business
& Economic Statistics29, no. 2 (2011): 238-49, where clustering
is grouped on city and year. All specifications includes fixed
effects for city (30 overall), year (2003-2012), and interview
month. Individual covariates are identical to that in Table A.2,
except when stratifying on covariate under consideration.

Notes

1. K. M. Flegal, M. D. Carroll, R. J. Kuczmarski,
and C. L. Johnson, “Overweight and Obesity in the United
States: Prevalence and Trends, 1960-1994,” International
Journal of Obesity and Related Metabolic Disorders: Journal of the
International Association for the Study of Obesity 22, no. 1
(1998): 39-47.

28. The CDC reports that one data limitation of the
Behavioral Risk Factor Surveillance System is that “Reliance
on self-reported heights and weights to calculate the BMI is likely
to underestimate average BMI and the proportion of the population
in higher BMI categories in population surveys.” See Centers
for Disease Control and Prevention, “Diabetes Public Health
Resource: Methods and Limitations,” October 15, 2014,
http://www.cdc.gov/diabetes/statistics/comp/methods.htm.
It is possible that the small, temporary effects of menu mandates
are due to temporarily changing social norms or awareness, rather
than an actual temporary reduction in body weight. There is little
reason to think, however, that people that people in cities with
menu mandates systematically differ in their reporting of weight
compared to those in other cities. Also, even if people understate
their weight in levels, it isn’t clear that changes in weight
(i.e., from the difference-in-difference framework) will be
affected. If everyone reports their weight as 5 lbs. lower than it
really is, the change in body weight pre/post menu mandate will be
unaffected. In either case, the effects fade out quickly.

38. U.S. Census Bureau, Population Division,
“Annual Estimates of the Resident Population for Incorporated
Places Over 50,000, Ranked by July 1, 2012, Population: April 1,
2010 to July 1, 2012,” May 2013.

39. All statistics are unweighted. Other
characteristics, such as age, vary within the sample over time.
Such characteristics are controlled for in the regressions.

41. See “Menu-Labeling Laws,”
Menu-Calc, http://www.menucalc.com/menulabeling.aspx.
Nashville passed, but did not implement, a menu mandate. Several
other counties in New York passed menu mandates, but none of the
cities in those counties are in the top 30 cities.

42. With the exception of Portland, Oregon, neither
Maine’s law nor Oregon’s law affected any of the 30
largest cities.

46. In addition, all specifications have been
estimated also including city-specific time trends. The key
substantive conclusion—that such mandates have small or
insignificant effects on BMI and other outcomes—holds even
more forcefully with the inclusion of such trends.

Aaron Yelowitz is associate professor in the department of
economics and a joint faculty member in the Martin School of Public
Policy and Administration at the University of Kentucky. He is also
an adjunct scholar with the Cato Institute.